Overview

Dataset statistics

Number of variables15
Number of observations50
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory122.6 B

Variable types

DateTime1
Numeric14

Alerts

Adj Close is highly correlated with 5MA and 9 other fieldsHigh correlation
H-L is highly correlated with 7SD and 1 other fieldsHigh correlation
O-C is highly correlated with ReturnsHigh correlation
5MA is highly correlated with Adj Close and 8 other fieldsHigh correlation
10MA is highly correlated with Adj Close and 7 other fieldsHigh correlation
20MA is highly correlated with Adj Close and 7 other fieldsHigh correlation
7SD is highly correlated with Adj Close and 9 other fieldsHigh correlation
RSI_14 is highly correlated with Adj CloseHigh correlation
EMA8 is highly correlated with Adj Close and 8 other fieldsHigh correlation
EMA21 is highly correlated with Adj Close and 7 other fieldsHigh correlation
EMA34 is highly correlated with Adj Close and 8 other fieldsHigh correlation
EMA55 is highly correlated with Adj Close and 7 other fieldsHigh correlation
Returns is highly correlated with O-CHigh correlation
Volume is highly correlated with Adj Close and 5 other fieldsHigh correlation
Adj Close is highly correlated with 5MA and 9 other fieldsHigh correlation
H-L is highly correlated with 7SD and 1 other fieldsHigh correlation
O-C is highly correlated with ReturnsHigh correlation
5MA is highly correlated with Adj Close and 8 other fieldsHigh correlation
10MA is highly correlated with Adj Close and 8 other fieldsHigh correlation
20MA is highly correlated with Adj Close and 7 other fieldsHigh correlation
7SD is highly correlated with Adj Close and 9 other fieldsHigh correlation
RSI_14 is highly correlated with Adj CloseHigh correlation
EMA8 is highly correlated with Adj Close and 8 other fieldsHigh correlation
EMA21 is highly correlated with Adj Close and 8 other fieldsHigh correlation
EMA34 is highly correlated with Adj Close and 8 other fieldsHigh correlation
EMA55 is highly correlated with Adj Close and 8 other fieldsHigh correlation
Returns is highly correlated with O-CHigh correlation
Volume is highly correlated with Adj Close and 8 other fieldsHigh correlation
Adj Close is highly correlated with 5MA and 7 other fieldsHigh correlation
H-L is highly correlated with VolumeHigh correlation
O-C is highly correlated with ReturnsHigh correlation
5MA is highly correlated with Adj Close and 6 other fieldsHigh correlation
10MA is highly correlated with Adj Close and 6 other fieldsHigh correlation
20MA is highly correlated with Adj Close and 6 other fieldsHigh correlation
7SD is highly correlated with VolumeHigh correlation
RSI_14 is highly correlated with Adj CloseHigh correlation
EMA8 is highly correlated with Adj Close and 6 other fieldsHigh correlation
EMA21 is highly correlated with Adj Close and 6 other fieldsHigh correlation
EMA34 is highly correlated with Adj Close and 6 other fieldsHigh correlation
EMA55 is highly correlated with Adj Close and 6 other fieldsHigh correlation
Returns is highly correlated with O-CHigh correlation
Volume is highly correlated with H-L and 1 other fieldsHigh correlation
Date is highly correlated with Adj Close and 13 other fieldsHigh correlation
Adj Close is highly correlated with Date and 13 other fieldsHigh correlation
H-L is highly correlated with Date and 12 other fieldsHigh correlation
O-C is highly correlated with Date and 9 other fieldsHigh correlation
5MA is highly correlated with Date and 12 other fieldsHigh correlation
10MA is highly correlated with Date and 11 other fieldsHigh correlation
20MA is highly correlated with Date and 12 other fieldsHigh correlation
7SD is highly correlated with Date and 13 other fieldsHigh correlation
RSI_14 is highly correlated with Date and 9 other fieldsHigh correlation
EMA8 is highly correlated with Date and 12 other fieldsHigh correlation
EMA21 is highly correlated with Date and 13 other fieldsHigh correlation
EMA34 is highly correlated with Date and 13 other fieldsHigh correlation
EMA55 is highly correlated with Date and 13 other fieldsHigh correlation
Returns is highly correlated with Date and 10 other fieldsHigh correlation
Volume is highly correlated with Date and 10 other fieldsHigh correlation
Date has unique values Unique
Adj Close has unique values Unique
H-L has unique values Unique
O-C has unique values Unique
5MA has unique values Unique
10MA has unique values Unique
20MA has unique values Unique
7SD has unique values Unique
RSI_14 has unique values Unique
EMA8 has unique values Unique
EMA21 has unique values Unique
EMA34 has unique values Unique
EMA55 has unique values Unique
Returns has unique values Unique
Volume has unique values Unique

Reproduction

Analysis started2022-02-13 09:51:34.106408
Analysis finished2022-02-13 09:51:48.379089
Duration14.27 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Date
Date

HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size528.0 B
Minimum2021-12-02 00:00:00
Maximum2022-02-11 00:00:00
2022-02-13T17:51:48.424951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:48.509711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Adj Close
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3235.012817
Minimum2776.909912
Maximum3523.290039
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:48.595481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2776.909912
5-th percentile2795.886487
Q13132.682495
median3295.639893
Q33398.610046
95-th percentile3475.715979
Maximum3523.290039
Range746.380127
Interquartile range (IQR)265.9275513

Descriptive statistics

Standard deviation214.0619038
Coefficient of variation (CV)0.06617034179
Kurtosis-0.3429209855
Mean3235.012817
Median Absolute Deviation (MAD)117.4349365
Skewness-0.848071439
Sum161750.6409
Variance45822.49867
MonotonicityNot monotonic
2022-02-13T17:51:48.674270image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3437.3601071
 
2.0%
2777.4499511
 
2.0%
3307.239991
 
2.0%
3304.1398931
 
2.0%
3224.2800291
 
2.0%
3242.760011
 
2.0%
3178.3500981
 
2.0%
3125.979981
 
2.0%
3033.3500981
 
2.0%
2852.8601071
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
2776.9099121
2.0%
2777.4499511
2.0%
2792.751
2.0%
2799.7199711
2.0%
2852.8601071
2.0%
2879.5600591
2.0%
2890.8798831
2.0%
2991.4699711
2.0%
3012.251
2.0%
3023.8701171
2.0%
ValueCountFrequency (%)
3523.2900391
2.0%
3523.1599121
2.0%
3483.4199221
2.0%
3466.3000491
2.0%
3444.239991
2.0%
3437.3601071
2.0%
3427.3701171
2.0%
3421.3701171
2.0%
3420.739991
2.0%
3413.2199711
2.0%

H-L
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.34780273
Minimum36.5
Maximum211.8400879
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:48.761035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum36.5
5-th percentile46.29550781
Q159.34753418
median94.3651123
Q3120.9274902
95-th percentile172.7695923
Maximum211.8400879
Range175.3400879
Interquartile range (IQR)61.57995605

Descriptive statistics

Standard deviation41.54854574
Coefficient of variation (CV)0.4403764002
Kurtosis0.2633317047
Mean94.34780273
Median Absolute Deviation (MAD)32.07006836
Skewness0.7968045901
Sum4717.390137
Variance1726.281653
MonotonicityNot monotonic
2022-02-13T17:51:48.837830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.949951171
 
2.0%
157.3298341
 
2.0%
112.96997071
 
2.0%
49.21997071
 
2.0%
102.60986331
 
2.0%
48.989990231
 
2.0%
41.399902341
 
2.0%
601
 
2.0%
132.97998051
 
2.0%
176.59008791
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
36.51
2.0%
41.399902341
2.0%
45.489990231
2.0%
47.28002931
2.0%
48.590087891
2.0%
48.989990231
2.0%
49.21997071
2.0%
52.229980471
2.0%
55.390136721
2.0%
55.830078121
2.0%
ValueCountFrequency (%)
211.84008791
2.0%
191.85986331
2.0%
176.59008791
2.0%
168.10009771
2.0%
157.3298341
2.0%
135.21997071
2.0%
132.97998051
2.0%
131.27001951
2.0%
125.32006841
2.0%
124.84008791
2.0%

O-C
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.742783203
Minimum-110.8798828
Maximum146.1398926
Zeros0
Zeros (%)0.0%
Negative22
Negative (%)44.0%
Memory size528.0 B
2022-02-13T17:51:48.919610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-110.8798828
5-th percentile-93.85405273
Q1-33.60009766
median16.40002441
Q349.10748291
95-th percentile99.65240479
Maximum146.1398926
Range257.0197754
Interquartile range (IQR)82.70758057

Descriptive statistics

Standard deviation59.28510632
Coefficient of variation (CV)6.085027767
Kurtosis-0.423688411
Mean9.742783203
Median Absolute Deviation (MAD)41.68005371
Skewness-0.007295616378
Sum487.1391602
Variance3514.723831
MonotonicityNot monotonic
2022-02-13T17:51:49.001392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22.639892581
 
2.0%
117.55004881
 
2.0%
-77.239990231
 
2.0%
27.360107421
 
2.0%
80.729980471
 
2.0%
-39.760009771
 
2.0%
3.751
 
2.0%
49.260009771
 
2.0%
101.96997071
 
2.0%
146.13989261
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
-110.87988281
2.0%
-96.46997071
2.0%
-94.340087891
2.0%
-93.260009771
2.0%
-77.239990231
2.0%
-62.350097661
2.0%
-57.090087891
2.0%
-51.330078121
2.0%
-46.140136721
2.0%
-40.660156251
2.0%
ValueCountFrequency (%)
146.13989261
2.0%
117.55004881
2.0%
101.96997071
2.0%
96.819824221
2.0%
89.950195311
2.0%
88.760009771
2.0%
80.729980471
2.0%
65.209960941
2.0%
64.100097661
2.0%
58.320068361
2.0%

5MA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3248.789815
Minimum2822.731982
Maximum3490.856055
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:49.082173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2822.731982
5-th percentile2858.387896
Q13092.018542
median3320.692017
Q33405.773511
95-th percentile3471.433298
Maximum3490.856055
Range668.1240723
Interquartile range (IQR)313.7549683

Descriptive statistics

Standard deviation207.0345004
Coefficient of variation (CV)0.06372665274
Kurtosis-0.7624980704
Mean3248.789815
Median Absolute Deviation (MAD)121.992041
Skewness-0.7641308371
Sum162439.4908
Variance42863.28436
MonotonicityNot monotonic
2022-02-13T17:51:49.159963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3490.8560551
 
2.0%
2870.8520021
 
2.0%
3268.0520021
 
2.0%
3271.4520021
 
2.0%
3263.2919921
 
2.0%
3261.6279791
 
2.0%
3251.3540041
 
2.0%
3215.1020021
 
2.0%
3160.9440431
 
2.0%
3086.6600591
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
2822.7319821
2.0%
2828.0719731
2.0%
2848.189991
2.0%
2870.8520021
2.0%
2893.020021
2.0%
2936.8120121
2.0%
2939.9800291
2.0%
2940.5580081
2.0%
2991.4580081
2.0%
3016.2840331
2.0%
ValueCountFrequency (%)
3490.8560551
2.0%
3480.2959961
2.0%
3473.0919921
2.0%
3469.4060061
2.0%
3467.9020511
2.0%
3460.1940431
2.0%
3444.81
2.0%
3444.3060551
2.0%
3441.0620611
2.0%
3433.4280271
2.0%

10MA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3271.501136
Minimum2879.771997
Maximum3555.993042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:49.242748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2879.771997
5-th percentile2912.925801
Q13085.03902
median3354.483496
Q33430.42027
95-th percentile3498.228444
Maximum3555.993042
Range676.2210449
Interquartile range (IQR)345.38125

Descriptive statistics

Standard deviation204.4941524
Coefficient of variation (CV)0.06250774306
Kurtosis-1.018812505
Mean3271.501136
Median Absolute Deviation (MAD)107.7500122
Skewness-0.649504514
Sum163575.0568
Variance41817.85838
MonotonicityNot monotonic
2022-02-13T17:51:49.319537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3555.9930421
 
2.0%
3042.9770021
 
2.0%
3319.0040041
 
2.0%
3311.0159911
 
2.0%
3296.1550051
 
2.0%
3286.9969971
 
2.0%
3264.0229981
 
2.0%
3241.5770021
 
2.0%
3216.1980221
 
2.0%
3174.9760251
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
2879.7719971
2.0%
2905.4160161
2.0%
2909.764991
2.0%
2916.7890141
2.0%
2932.2370121
2.0%
2936.5479981
2.0%
2957.3660161
2.0%
2979.4030031
2.0%
2991.8380131
2.0%
3024.0370121
2.0%
ValueCountFrequency (%)
3555.9930421
2.0%
3525.366041
2.0%
3500.4460451
2.0%
3495.5180421
2.0%
3489.8300291
2.0%
3480.131031
2.0%
3474.0990231
2.0%
3457.0770261
2.0%
3446.8110351
2.0%
3444.5530271
2.0%

20MA
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3331.377846
Minimum3019.38302
Maximum3536.81051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:49.401284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3019.38302
5-th percentile3035.384631
Q13188.937628
median3381.469019
Q33464.817157
95-th percentile3528.812545
Maximum3536.81051
Range517.4274902
Interquartile range (IQR)275.8795288

Descriptive statistics

Standard deviation173.1137352
Coefficient of variation (CV)0.05196460539
Kurtosis-1.014080684
Mean3331.377846
Median Absolute Deviation (MAD)121.2255127
Skewness-0.6183827713
Sum166568.8923
Variance29968.3653
MonotonicityNot monotonic
2022-02-13T17:51:49.488054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3536.810511
 
2.0%
3180.9905031
 
2.0%
3360.7290161
 
2.0%
3356.8445071
 
2.0%
3344.7435061
 
2.0%
3338.010511
 
2.0%
3326.910511
 
2.0%
3316.1305051
 
2.0%
3297.3810061
 
2.0%
3268.9870121
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
3019.383021
2.0%
3027.3035161
2.0%
3033.5070071
2.0%
3037.6795041
2.0%
3037.7520021
2.0%
3042.3705081
2.0%
3047.985011
2.0%
3073.4965091
2.0%
3090.4060061
2.0%
3109.6170041
2.0%
ValueCountFrequency (%)
3536.810511
2.0%
3532.4500121
2.0%
3529.5845211
2.0%
3527.8690191
2.0%
3526.9995121
2.0%
3526.9310181
2.0%
3525.5865111
2.0%
3518.8965211
2.0%
3510.7040281
2.0%
3506.9840331
2.0%

7SD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.79167241
Minimum18.87363683
Maximum172.8201218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:49.660618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum18.87363683
5-th percentile28.16960394
Q140.78246222
median58.31007927
Q3107.5316363
95-th percentile160.5242515
Maximum172.8201218
Range153.946485
Interquartile range (IQR)66.74917403

Descriptive statistics

Standard deviation46.34288012
Coefficient of variation (CV)0.6114508183
Kurtosis-0.6814903827
Mean75.79167241
Median Absolute Deviation (MAD)22.41087577
Skewness0.8754549764
Sum3789.583621
Variance2147.662538
MonotonicityNot monotonic
2022-02-13T17:51:49.738410image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.476690981
 
2.0%
160.90285451
 
2.0%
61.909998771
 
2.0%
40.313686861
 
2.0%
33.853004711
 
2.0%
33.597349231
 
2.0%
45.544047511
 
2.0%
64.737108671
 
2.0%
98.696767981
 
2.0%
152.77855541
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
18.873636831
2.0%
27.651737421
2.0%
28.075444181
2.0%
28.284688081
2.0%
29.141567381
2.0%
29.685882441
2.0%
31.336962671
2.0%
33.597349231
2.0%
33.853004711
2.0%
37.945402291
2.0%
ValueCountFrequency (%)
172.82012181
2.0%
161.42888251
2.0%
160.90285451
2.0%
160.06151461
2.0%
157.5369881
2.0%
157.37989441
2.0%
152.77855541
2.0%
149.59442441
2.0%
137.59748351
2.0%
136.48289921
2.0%

RSI_14
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.79046765
Minimum21.16875324
Maximum55.88630939
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:49.827169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum21.16875324
5-th percentile22.69585717
Q137.7044936
median43.77010801
Q347.60389322
95-th percentile53.17141346
Maximum55.88630939
Range34.71755614
Interquartile range (IQR)9.899399626

Descriptive statistics

Standard deviation8.965423394
Coefficient of variation (CV)0.2145327367
Kurtosis0.137562387
Mean41.79046765
Median Absolute Deviation (MAD)4.375806534
Skewness-0.7883672761
Sum2089.523382
Variance80.37881663
MonotonicityNot monotonic
2022-02-13T17:51:49.904963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45.157153581
 
2.0%
21.590383191
 
2.0%
44.010626611
 
2.0%
43.756469571
 
2.0%
37.714352751
 
2.0%
39.786397281
 
2.0%
35.369874241
 
2.0%
32.236519571
 
2.0%
27.582190871
 
2.0%
21.168753241
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
21.168753241
2.0%
21.590383191
2.0%
22.241082981
2.0%
23.251692291
2.0%
25.118573511
2.0%
27.582190871
2.0%
32.04880451
2.0%
32.155288761
2.0%
32.236519571
2.0%
35.282166041
2.0%
ValueCountFrequency (%)
55.886309391
2.0%
55.651026831
2.0%
53.293030421
2.0%
53.022770521
2.0%
52.988818181
2.0%
52.977893261
2.0%
52.780543811
2.0%
49.613700711
2.0%
49.611488091
2.0%
48.622311621
2.0%

EMA8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3264.549695
Minimum2908.810322
Maximum3506.969473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:49.987737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2908.810322
5-th percentile2924.04891
Q13099.548948
median3328.753907
Q33411.844949
95-th percentile3485.370747
Maximum3506.969473
Range598.1591514
Interquartile range (IQR)312.2960006

Descriptive statistics

Standard deviation196.834836
Coefficient of variation (CV)0.06029463613
Kurtosis-1.152418042
Mean3264.549695
Median Absolute Deviation (MAD)124.7806629
Skewness-0.5941348578
Sum163227.4847
Variance38743.95265
MonotonicityNot monotonic
2022-02-13T17:51:50.061543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3506.9694731
 
2.0%
2952.7154091
 
2.0%
3300.9825761
 
2.0%
3301.6842021
 
2.0%
3284.4832751
 
2.0%
3275.2114381
 
2.0%
3253.6866961
 
2.0%
3225.3074261
 
2.0%
3182.6502421
 
2.0%
3109.3635451
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
2908.8103221
2.0%
2917.167541
2.0%
2921.4878191
2.0%
2927.1791331
2.0%
2948.6660181
2.0%
2952.7154091
2.0%
2962.7957921
2.0%
2972.8883121
2.0%
3002.7912541
2.0%
3014.1820121
2.0%
ValueCountFrequency (%)
3506.9694731
2.0%
3490.4356311
2.0%
3488.8765841
2.0%
3481.0858361
2.0%
3480.9295991
2.0%
3478.9573411
2.0%
3469.0274921
2.0%
3459.4890651
2.0%
3447.5800761
2.0%
3442.2315121
2.0%

EMA21
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3319.008183
Minimum3037.826641
Maximum3506.199159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:50.140327image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3037.826641
5-th percentile3060.837669
Q13141.864126
median3379.669102
Q33445.675861
95-th percentile3494.794885
Maximum3506.199159
Range468.3725184
Interquartile range (IQR)303.8117354

Descriptive statistics

Standard deviation162.1999457
Coefficient of variation (CV)0.04887000475
Kurtosis-1.259593707
Mean3319.008183
Median Absolute Deviation (MAD)96.55841544
Skewness-0.6001300018
Sum165950.4092
Variance26308.82238
MonotonicityNot monotonic
2022-02-13T17:51:50.217089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3506.1991591
 
2.0%
3132.9759751
 
2.0%
3355.631581
 
2.0%
3350.9505171
 
2.0%
3339.4350181
 
2.0%
3330.6463811
 
2.0%
3316.8012651
 
2.0%
3299.4538751
 
2.0%
3275.2626231
 
2.0%
3236.8623941
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
3037.8266411
2.0%
3048.2778591
2.0%
3058.3171411
2.0%
3063.9183141
2.0%
3069.0851451
2.0%
3073.6066481
2.0%
3073.7674031
2.0%
3081.8203151
2.0%
3087.4058241
2.0%
3093.1062351
2.0%
ValueCountFrequency (%)
3506.1991591
2.0%
3495.6165121
2.0%
3495.2800731
2.0%
3494.2018771
2.0%
3492.4920891
2.0%
3489.6598881
2.0%
3489.4122941
2.0%
3480.7226341
2.0%
3471.7324021
2.0%
3471.2385511
2.0%

EMA34
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3348.892211
Minimum3127.873466
Maximum3481.680163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:50.293915image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3127.873466
5-th percentile3133.479791
Q13225.058765
median3402.364569
Q33451.253897
95-th percentile3478.338938
Maximum3481.680163
Range353.8066962
Interquartile range (IQR)226.1951323

Descriptive statistics

Standard deviation128.5557018
Coefficient of variation (CV)0.03838753048
Kurtosis-1.147113792
Mean3348.892211
Median Absolute Deviation (MAD)67.50017055
Skewness-0.7002582292
Sum167444.6106
Variance16526.56848
MonotonicityNot monotonic
2022-02-13T17:51:50.367719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3481.6801631
 
2.0%
3218.3780361
 
2.0%
3383.6716571
 
2.0%
3379.1269841
 
2.0%
3370.2785871
 
2.0%
3362.9918111
 
2.0%
3352.4408561
 
2.0%
3339.5002341
 
2.0%
3322.0059411
 
2.0%
3295.1976071
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
3127.8734661
2.0%
3129.2972711
2.0%
3130.9779961
2.0%
3136.537541
2.0%
3139.2771531
2.0%
3141.5233971
2.0%
3143.7260641
2.0%
3149.1439851
2.0%
3157.440591
2.0%
3165.535771
2.0%
ValueCountFrequency (%)
3481.6801631
2.0%
3479.3772241
2.0%
3479.1322121
2.0%
3477.3693821
2.0%
3476.4638661
2.0%
3476.4292981
2.0%
3473.6259161
2.0%
3472.4539941
2.0%
3467.2754851
2.0%
3467.2197451
2.0%

EMA55
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3371.10557
Minimum3204.060754
Maximum3458.079689
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:50.447501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum3204.060754
5-th percentile3209.438543
Q13290.800376
median3414.046604
Q33444.451114
95-th percentile3456.365396
Maximum3458.079689
Range254.0189355
Interquartile range (IQR)153.6507378

Descriptive statistics

Standard deviation92.28359188
Coefficient of variation (CV)0.02737487449
Kurtosis-0.9837061214
Mean3371.10557
Median Absolute Deviation (MAD)39.03411781
Skewness-0.8108924411
Sum168555.2785
Variance8516.26133
MonotonicityNot monotonic
2022-02-13T17:51:50.528284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3455.4172591
 
2.0%
3286.0907131
 
2.0%
3401.1288371
 
2.0%
3397.6649211
 
2.0%
3391.4725591
 
2.0%
3386.1613611
 
2.0%
3378.7394811
 
2.0%
3369.7122981
 
2.0%
3357.6992891
 
2.0%
3339.6692111
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
3204.0607541
2.0%
3209.0701171
2.0%
3209.1789441
2.0%
3209.7558311
2.0%
3210.2570541
2.0%
3210.9353161
2.0%
3213.0888541
2.0%
3229.2437011
2.0%
3237.2805421
2.0%
3245.1846691
2.0%
ValueCountFrequency (%)
3458.0796891
2.0%
3457.5854061
2.0%
3457.1411461
2.0%
3455.4172591
2.0%
3455.2198221
2.0%
3454.6959621
2.0%
3453.088061
2.0%
3453.0733831
2.0%
3452.598721
2.0%
3452.1553921
2.0%

Returns
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.001917166766
Minimum-0.07812767463
Maximum0.1353591362
Zeros0
Zeros (%)0.0%
Negative31
Negative (%)62.0%
Memory size528.0 B
2022-02-13T17:51:50.609068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0.07812767463
5-th percentile-0.03394125837
Q1-0.01497681334
median-0.003565910773
Q30.006552877878
95-th percentile0.02969014528
Maximum0.1353591362
Range0.2134868108
Interquartile range (IQR)0.02152969121

Descriptive statistics

Standard deviation0.02911997047
Coefficient of variation (CV)-15.18906492
Kurtosis9.96963657
Mean-0.001917166766
Median Absolute Deviation (MAD)0.01107264988
Skewness1.745934639
Sum-0.09585833832
Variance0.0008479726801
MonotonicityNot monotonic
2022-02-13T17:51:50.681876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.001846800361
 
2.0%
-0.0079543739251
 
2.0%
0.024002086941
 
2.0%
-0.00093736700861
 
2.0%
-0.024169637451
 
2.0%
0.0057315060421
 
2.0%
-0.019862682381
 
2.0%
-0.016477139261
 
2.0%
-0.029632270011
 
2.0%
-0.059501865731
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
-0.078127674631
2.0%
-0.059501865731
2.0%
-0.035911143061
2.0%
-0.031533621531
2.0%
-0.029632270011
2.0%
-0.025641209851
2.0%
-0.024169637451
2.0%
-0.019862682381
2.0%
-0.018893055821
2.0%
-0.017283520181
2.0%
ValueCountFrequency (%)
0.13535913621
2.0%
0.038863545071
2.0%
0.031084077911
2.0%
0.027986449841
2.0%
0.024977591641
2.0%
0.024002086941
2.0%
0.022118319681
2.0%
0.02202166691
2.0%
0.019978575471
2.0%
0.013326897911
2.0%

Volume
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3754814
Minimum1787700
Maximum12640500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2022-02-13T17:51:50.761662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1787700
5-th percentile2051775
Q12736875
median3225700
Q33869750
95-th percentile8010770
Maximum12640500
Range10852800
Interquartile range (IQR)1132875

Descriptive statistics

Standard deviation2082380.286
Coefficient of variation (CV)0.5545894646
Kurtosis9.282704117
Mean3754814
Median Absolute Deviation (MAD)596300
Skewness2.907366948
Sum187740700
Variance4.336307656 × 1012
MonotonicityNot monotonic
2022-02-13T17:51:50.855406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32363001
 
2.0%
47801001
 
2.0%
31403001
 
2.0%
25015001
 
2.0%
26094001
 
2.0%
22958001
 
2.0%
33646001
 
2.0%
26621001
 
2.0%
35987001
 
2.0%
81986001
 
2.0%
Other values (40)40
80.0%
ValueCountFrequency (%)
17877001
2.0%
18394001
2.0%
18792001
2.0%
22627001
2.0%
22958001
2.0%
23031001
2.0%
23293001
2.0%
23915001
2.0%
25015001
2.0%
25979001
2.0%
ValueCountFrequency (%)
126405001
2.0%
112766001
2.0%
81986001
2.0%
77812001
2.0%
51312001
2.0%
47801001
2.0%
45412001
2.0%
43899001
2.0%
43665001
2.0%
42771001
2.0%

Interactions

2022-02-13T17:51:47.215193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:35.985358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.906881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.844393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.669174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.642522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.510223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.391822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.226608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.130148image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.927042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.789714image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.588539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.473189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.275038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.055199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.974694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.905230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.734967image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.704388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.572053image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.453684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.287470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.190009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.984847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.850523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.646432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.529010image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.342857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.119992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.033539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.962070image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.802783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.765192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.632891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.511530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.345260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.245860image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.040721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.907395image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.701236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.580898image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.397672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.178870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.093380image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.016899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.867636image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.828027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.685750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.568348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.399113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.299690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.092582image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.961246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.758112image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.630736image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.460533image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.245686image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.156211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.076731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.934463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.889858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.746585image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.635164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.458990image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.359568image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.150402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.021092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.816922image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.686586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.517349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.307521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.218071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.134608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.994296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.959702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.802430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.698993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.516795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.415379image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.204284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.078937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.873795image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.742437image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.572237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.372365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.275919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.192425image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.054104image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.017514image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.857260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.758873image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.572676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.471265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.257150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.135787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.929622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.795321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.632082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.442157image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.338750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.253297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.117936image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.080376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.916098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.820698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.633515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.531071image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.313987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.194626image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.988490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.852172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.689910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.505991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.399633image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.318084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.180802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.150158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.973981image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.880535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.692360image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.589939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.370846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.254436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.053317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.907027image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.749730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.582753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.458427image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.386902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.242635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.209052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.030824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.940377image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.754159image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.645785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.424690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.311311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.108180image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.960871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.802583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.646607image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.514278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.449783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.299492image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.265847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.081691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.995224image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.808042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.698616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.476549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.365184image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.161997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.010715image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.859433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.708446image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.575111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.505610image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.361316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.325724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.137506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.054042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.866881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.758487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.530385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.422015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.218875image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.063572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.914307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.776261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.633955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.561460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.423118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.390516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.192354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.111916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.023456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.813308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.583243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.477857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.274724image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.116460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.964183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:36.845074image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:37.787543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:38.612325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:39.581771image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:40.449352image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:41.336998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:42.165739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.073323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:43.866204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:44.736826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:45.530723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:46.418345image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-02-13T17:51:47.162307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-02-13T17:51:50.928225image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-02-13T17:51:51.132630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-02-13T17:51:51.240375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-02-13T17:51:51.349047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-02-13T17:51:48.159661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-02-13T17:51:48.321193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

DateAdj CloseH-LO-C5MA10MA20MA7SDRSI_14EMA8EMA21EMA34EMA55ReturnsVolume
02021-12-023437.36010768.94995122.6398933490.8560553555.9930423536.81051060.47669145.1571543506.9694733506.1991593481.6801633455.417259-0.0018473236300
12021-12-033389.790039131.27002065.2099613467.9020513525.3660403532.45001269.22197741.7817923480.9295993495.6165123476.4292983453.073383-0.0138394032600
22021-12-063427.370117135.219971-34.3701173441.0620613500.4460453527.86901959.03587145.2626483469.0274923489.4122943473.6259163452.1553920.0110863443000
32021-12-073523.29003983.300049-31.2900393444.3060553495.5180423529.58452161.38137452.9888183481.0858363492.4920893476.4638663454.6959620.0279863320500
42021-12-083523.15991248.590088-0.1499023460.1940433489.8300293526.93101852.97490752.9778933490.4356313495.2800733479.1322123457.141146-0.0000372262700
52021-12-093483.41992256.59985431.5800783469.4060063480.1310303526.99951250.50685149.6137013488.8765843494.2018773479.3772243458.079689-0.0112802303100
62021-12-103444.239990108.54003964.1000983480.2959963474.0990233525.58651150.47730046.4798963478.9573413489.6598883477.3693823457.585406-0.0112483031400
72021-12-133391.35009859.39990248.6499023473.0919923457.0770263518.89652156.71423042.5708053459.4890653480.7226343472.4539943455.219822-0.0153563108500
82021-12-143381.83007861.179932-30.8300783444.8000003444.5530273510.70402858.28959041.8879173442.2315123471.7324023467.2754853452.598720-0.0028072798800
92021-12-153466.300049168.100098-94.3400883433.4280273446.8110353506.98403357.22592849.6114883447.5800763471.2385513467.2197453453.0880600.0249783789700

Last rows

DateAdj CloseH-LO-C5MA10MA20MA7SDRSI_14EMA8EMA21EMA34EMA55ReturnsVolume
402022-01-312991.469971121.199951-96.4699712848.1899902932.2370123109.61700474.68169741.3774622927.1791333073.6066483165.5357703245.1846690.0388643915400
412022-02-013023.87011781.609863-23.8701172893.0200202916.7890143090.40600698.16147243.7837462948.6660183069.0851453157.4405903237.2805420.0108312961000
422022-02-023012.250000124.22998088.7600102939.9800292905.4160163073.496509110.47659243.1004452962.7957923063.9183143149.1439853229.243701-0.0038434366500
432022-02-032776.909912118.29003957.8400882936.8120122879.7719973047.985010114.09191032.1552892921.4878193037.8266413127.8734663213.088854-0.07812811276600
442022-02-043152.790039211.840088-40.6601562991.4580082909.7649903042.370508136.48289952.7805442972.8883123048.2778593129.2972713210.9353160.13535912640500
452022-02-073158.709961108.82006811.6899413024.9060062936.5479983037.752002137.59748353.0227713014.1820123058.3171413130.9779963209.0701170.0018785131200
462022-02-083228.270020124.840088-93.2600103065.7859862979.4030033037.679504149.59442455.8863093061.7571253073.7674033136.5375403209.7558310.0220223802000
472022-02-093223.79003971.68994133.6799323108.0939943024.0370123033.507007160.06151555.6510273097.7644393087.4058243141.5233973210.257054-0.0013883439300
482022-02-103180.07006859.330078-13.0700683188.7260253062.7690193027.303516161.42888253.2930303116.0545793095.8298463143.7260643209.178944-0.0135623413400
492022-02-113065.870117125.32006896.8198243171.3420413081.4000243019.383020157.53698847.6174903104.9024763093.1062353139.2771533204.060754-0.0359113851600